# coding=utf-8
# Copyright 2023 Antgroup and The HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" PyTorch BailingMoeLinear Model."""
import math
import warnings
from typing import List, Optional, Tuple, Union
from einops import rearrange, repeat

import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from torch import nn
from torch.nn import CrossEntropyLoss
from transformers.activations import ACT2FN
from transformers.cache_utils import Cache, DynamicCache
from transformers.modeling_attn_mask_utils import (
    AttentionMaskConverter, _prepare_4d_attention_mask,
    _prepare_4d_causal_attention_mask,
    _prepare_4d_causal_attention_mask_for_sdpa)
from transformers.modeling_outputs import (MoeCausalLMOutputWithPast,
                                           MoeModelOutputWithPast)
from transformers.modeling_utils import PreTrainedModel
from transformers.pytorch_utils import (ALL_LAYERNORM_LAYERS,
                                        is_torch_greater_or_equal_than_1_13)
from transformers.utils import (add_start_docstrings,
                                add_start_docstrings_to_model_forward,
                                is_flash_attn_2_available,
                                is_flash_attn_greater_or_equal_2_10, logging,
                                replace_return_docstrings)
from transformers.utils.import_utils import is_torch_fx_available
from .configuration_bailing_moe_linear import BailingMoeLinearConfig

if is_flash_attn_2_available():
    from flash_attn import flash_attn_func, flash_attn_varlen_func
    from flash_attn.bert_padding import (index_first_axis, pad_input,  # noqa
                                         unpad_input)
from fla.ops.simple_gla.fused_recurrent import fused_recurrent_simple_gla
from fla.ops.simple_gla.chunk import chunk_simple_gla

# This makes `_prepare_4d_causal_attention_mask` a leaf function in the FX graph.
# It means that the function will not be traced through and simply appear as a node in the graph.
if is_torch_fx_available():
    if not is_torch_greater_or_equal_than_1_13:
        import torch.fx

    _prepare_4d_causal_attention_mask = torch.fx.wrap(_prepare_4d_causal_attention_mask)


logger = logging.get_logger(__name__)

_CONFIG_FOR_DOC = "BailingMoeLinearConfig"


def _get_unpad_data(attention_mask):
    seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
    indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
    max_seqlen_in_batch = seqlens_in_batch.max().item()
    cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0))
    return (
        indices,
        cu_seqlens,
        max_seqlen_in_batch,
    )


def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
    warnings.warn(
        "Calling `transformers.models.BailingMoe.modeling_BailingMoe._prepare_4d_attention_mask` is deprecated and will be removed in v4.37. Use `transformers.modeling_attn_mask_utils._prepare_4d_attention_mask"
    )
    return _prepare_4d_attention_mask(mask=mask, dtype=dtype, tgt_len=tgt_len)


def _make_causal_mask(
    input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
):
    warnings.warn(
        "Calling `transformers.models.BailingMoe.modeling_BailingMoe._make_causal_mask` is deprecated and will be removed in v4.37. Use `transformers.models.BailingMoe.modeling_BailingMoe.AttentionMaskConverter._make_causal_mask"
    )
    return AttentionMaskConverter._make_causal_mask(
        input_ids_shape=input_ids_shape, dtype=dtype, device=device, past_key_values_length=past_key_values_length
    )


class BailingMoeRMSNorm(nn.Module):
    def __init__(self, hidden_size, eps=1e-6):
        """
        BailingMoeRMSNorm is equivalent to T5LayerNorm
        """
        super().__init__()
        self.weight = nn.Parameter(torch.ones(hidden_size))
        self.variance_epsilon = eps

    def forward(self, hidden_states):
        input_dtype = hidden_states.dtype
        hidden_states = hidden_states.to(torch.float32)
        variance = hidden_states.pow(2).mean(-1, keepdim=True)
        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
        return self.weight * hidden_states.to(input_dtype)

ALL_LAYERNORM_LAYERS.append(BailingMoeRMSNorm)


class BailingMoeRotaryEmbedding(nn.Module):
    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
        super().__init__()

        self.dim = dim
        self.max_position_embeddings = max_position_embeddings
        self.base = base
        inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
        self.register_buffer("inv_freq", inv_freq, persistent=False)

        # Build here to make `torch.jit.trace` work.
        self._set_cos_sin_cache(
            seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
        )
        self.max_seq_len_cached = None

    def _set_cos_sin_cache(self, seq_len, device, dtype):
        self.max_seq_len_cached = seq_len
        t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)

        freqs = torch.outer(t, self.inv_freq.to(t.device))
        # Different from paper, but it uses a different permutation in order to obtain the same calculation
        emb = torch.cat((freqs, freqs), dim=-1)
        self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
        self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)

    def forward(self, x, seq_len=None):
        # x: [bs, num_attention_heads, seq_len, head_size]
        if self.max_seq_len_cached is None or seq_len > self.max_seq_len_cached:
            self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)

        return (
            self.cos_cached[:seq_len].to(dtype=x.dtype),
            self.sin_cached[:seq_len].to(dtype=x.dtype),
        )


# Copied from transformers.models.llama.modeling_llama.LlamaLinearScalingRotaryEmbedding with Llama->BailingMoe
class BailingMoeLinearScalingRotaryEmbedding(BailingMoeRotaryEmbedding):
    """BailingMoeRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""

    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
        self.scaling_factor = scaling_factor
        super().__init__(dim, max_position_embeddings, base, device)

    def _set_cos_sin_cache(self, seq_len, device, dtype):
        self.max_seq_len_cached = seq_len
        t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
        t = t / self.scaling_factor

        freqs = torch.outer(t, self.inv_freq)
        # Different from paper, but it uses a different permutation in order to obtain the same calculation
        emb = torch.cat((freqs, freqs), dim=-1)
        self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
        self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)


# Copied from transformers.models.llama.modeling_llama.LlamaDynamicNTKScalingRotaryEmbedding with Llama->BailingMoe
class BailingMoeDynamicNTKScalingRotaryEmbedding(BailingMoeRotaryEmbedding):
    """BailingMoeRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""

    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
        self.scaling_factor = scaling_factor
        super().__init__(dim, max_position_embeddings, base, device)

    def _set_cos_sin_cache(self, seq_len, device, dtype):
        self.max_seq_len_cached = seq_len

        if seq_len > self.max_position_embeddings:
            base = self.base * (
                (self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)
            ) ** (self.dim / (self.dim - 2))
            inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
            self.register_buffer("inv_freq", inv_freq, persistent=False)

        t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)

        freqs = torch.outer(t, self.inv_freq)
        # Different from paper, but it uses a different permutation in order to obtain the same calculation
        emb = torch.cat((freqs, freqs), dim=-1)
        self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
        self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)


# Inverse dim formula to find dim based on number of rotations
def yarn_find_correction_dim(num_rotations, dim, base=10000, max_position_embeddings=2048):
    return (dim * math.log(max_position_embeddings / (num_rotations * 2 * math.pi))) / (2 * math.log(base))


# Find dim range bounds based on rotations
def yarn_find_correction_range(low_rot, high_rot, dim, base=10000, max_position_embeddings=2048):
    low = math.floor(yarn_find_correction_dim(low_rot, dim, base, max_position_embeddings))
    high = math.ceil(yarn_find_correction_dim(high_rot, dim, base, max_position_embeddings))
    return max(low, 0), min(high, dim - 1)  # Clamp values just in case


def yarn_get_mscale(scale=1, mscale=1):
    if scale <= 1:
        return 1.0
    return 0.1 * mscale * math.log(scale) + 1.0


def yarn_linear_ramp_mask(min, max, dim):
    if min == max:
        max += 0.001  # Prevent singularity

    linear_func = (torch.arange(dim, dtype=torch.float32) - min) / (max - min)
    ramp_func = torch.clamp(linear_func, 0, 1)
    return ramp_func


class BailingMoeYarnRotaryEmbedding(BailingMoeRotaryEmbedding):

    def __init__(
        self,
        dim,
        max_position_embeddings=2048,
        base=10000,
        device=None,
        scaling_factor=1.0,
        original_max_position_embeddings=4096,
        beta_fast=32,
        beta_slow=1,
        mscale=1,
        mscale_all_dim=0,
    ):
        self.scaling_factor = scaling_factor
        self.original_max_position_embeddings = original_max_position_embeddings
        self.beta_fast = beta_fast
        self.beta_slow = beta_slow
        self.mscale = mscale
        self.mscale_all_dim = mscale_all_dim
        super().__init__(dim, max_position_embeddings, base, device)

    def _set_cos_sin_cache(self, seq_len, device, dtype):
        self.max_seq_len_cached = seq_len
        dim = self.dim

        freq_extra = 1.0 / (self.base ** (torch.arange(0, dim, 2, dtype=torch.float32, device=device) / dim))
        freq_inter = 1.0 / (
            self.scaling_factor * self.base ** (torch.arange(0, dim, 2, dtype=torch.float32, device=device) / dim)
        )

        low, high = yarn_find_correction_range(
            self.beta_fast,
            self.beta_slow,
            dim,
            self.base,
            self.original_max_position_embeddings,
        )
        inv_freq_mask = 1.0 - yarn_linear_ramp_mask(low, high, dim // 2).to(device=device, dtype=torch.float32)
        inv_freq = freq_inter * (1 - inv_freq_mask) + freq_extra * inv_freq_mask
        self.register_buffer("inv_freq", inv_freq, persistent=False)

        t = torch.arange(seq_len, device=device, dtype=torch.float32)

        freqs = torch.outer(t, inv_freq)

        _mscale = float(
            yarn_get_mscale(self.scaling_factor, self.mscale)
            / yarn_get_mscale(self.scaling_factor, self.mscale_all_dim)
        )

        emb = torch.cat((freqs, freqs), dim=-1)
        self.register_buffer("cos_cached", (emb.cos() * _mscale).to(dtype), persistent=False)
        self.register_buffer("sin_cached", (emb.sin() * _mscale).to(dtype), persistent=False)


# Copied from transformers.models.llama.modeling_llama.rotate_half
def rotate_half(x):
    """Rotates half the hidden dims of the input."""
    x1 = x[..., : x.shape[-1] // 2]
    x2 = x[..., x.shape[-1] // 2 :]
    return torch.cat((-x2, x1), dim=-1)


# Copied from transformers.models.llama.modeling_llama.apply_rotary_pos_emb
def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
    """Applies Rotary Position Embedding to the query and key tensors.

    Args:
        q (`torch.Tensor`): The query tensor.
        k (`torch.Tensor`): The key tensor.
        cos (`torch.Tensor`): The cosine part of the rotary embedding.
        sin (`torch.Tensor`): The sine part of the rotary embedding.
        position_ids (`torch.Tensor`):
            The position indices of the tokens corresponding to the query and key tensors. For example, this can be
            used to pass offsetted position ids when working with a KV-cache.
        unsqueeze_dim (`int`, *optional*, defaults to 1):
            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
    Returns:
        `tuple(torch.Tensor)` comprising the query and key tensors rotated using the Rotary Position Embedding.
    """
    cos = cos[position_ids].unsqueeze(unsqueeze_dim)
    sin = sin[position_ids].unsqueeze(unsqueeze_dim)
    q_embed = (q * cos) + (rotate_half(q) * sin)
    k_embed = (k * cos) + (rotate_half(k) * sin)
    return q_embed, k_embed

class BailingMoeMLP(nn.Module):
    def __init__(self, config: BailingMoeLinearConfig, intermediate_size: int):
        super().__init__()
        self.config = config
        self.hidden_size = config.hidden_size
        self.intermediate_size = intermediate_size

        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
        self.act_fn = ACT2FN[config.hidden_act]

    def forward(self, x):
        return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))


class BailingMoeGate(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.config = config
        self.top_k = config.num_experts_per_tok
        self.num_experts = config.num_experts

        # topk selection algorithm
        self.norm_topk_prob = config.norm_topk_prob
        self.gating_dim = config.hidden_size
        self.weight = nn.Parameter(torch.empty((self.num_experts, self.gating_dim)))
        self.reset_parameters()

    def reset_parameters(self) -> None:
        import torch.nn.init as init
        init.kaiming_uniform_(self.weight, a=math.sqrt(5))

    def forward(self, hidden_states, sort=False):
        bsz, seq_len, h = hidden_states.shape
        # compute gating score
        hidden_states = hidden_states.view(-1, h)
        logits = F.linear(hidden_states, self.weight, None)
        scores = logits.softmax(dim=-1, dtype=torch.float32)

        # select top-k experts
        topk_weight, topk_idx = torch.topk(scores, k=self.top_k, dim=-1, sorted=sort)

        # norm gate to sum 1
        if self.top_k > 1 and self.norm_topk_prob:
            denominator = topk_weight.sum(dim=-1, keepdim=True)
            topk_weight = topk_weight / denominator

        return topk_idx, topk_weight, logits


class BailingMoeSparseMoeBlock(nn.Module):
    """
    A mixed expert module containing shared experts.
    """

    def __init__(self, config: BailingMoeLinearConfig):
        super().__init__()
        self.config = config
        self.num_experts_per_tok = config.num_experts_per_tok
        self._setup_experts()
        self.gate = BailingMoeGate(config)
        if config.num_shared_experts is not None:
            self.shared_experts = BailingMoeMLP(
                config=config, intermediate_size=config.moe_intermediate_size * config.num_shared_experts
            )

    def _setup_experts(self):
        self.experts = nn.ModuleList(
            [
                BailingMoeMLP(config=self.config, intermediate_size=self.config.moe_intermediate_size)
                for _ in range(self.config.num_experts)
            ]
        )

    def forward(self, hidden_states):
        identity = hidden_states
        bsz, seq_len, h = hidden_states.shape
        topk_idx, topk_weight, router_logits = self.gate(hidden_states)
        hidden_states = hidden_states.view(-1, hidden_states.shape[-1])
        flat_topk_idx = topk_idx.view(-1)
        if self.training:
            hidden_states = hidden_states.repeat_interleave(self.num_experts_per_tok, dim=0)
            y = torch.empty_like(hidden_states)
            for i, expert in enumerate(self.experts):
                y[flat_topk_idx == i] = expert(hidden_states[flat_topk_idx == i])
            y = (y.view(*topk_weight.shape, -1) * topk_weight.unsqueeze(-1)).sum(dim=1)
            y = y.to(hidden_states.dtype).view(bsz, seq_len, h)
        else:
            y = self.moe_infer(hidden_states, topk_idx, topk_weight).view(bsz, seq_len, h)
        if self.config.num_shared_experts is not None:
            y = y + self.shared_experts(identity)
        return y, (router_logits.view(bsz, seq_len, -1), topk_idx.view(bsz, seq_len, -1))

    @torch.no_grad()
    def moe_infer(self, x, topk_ids, topk_weight):
        cnts = topk_ids.new_zeros((topk_ids.shape[0], len(self.experts)))
        cnts.scatter_(1, topk_ids, 1)
        tokens_per_expert = cnts.sum(dim=0)
        idxs = topk_ids.view(-1).argsort()
        sorted_tokens = x[idxs // topk_ids.shape[1]]
        sorted_tokens_shape = sorted_tokens.shape
        tokens_per_expert = tokens_per_expert.cpu().numpy()
        outputs = []
        start_idx = 0
        for i, num_tokens in enumerate(tokens_per_expert):
            end_idx = start_idx + num_tokens
            if num_tokens == 0:
                continue
            expert = self.experts[i]
            tokens_for_this_expert = sorted_tokens[start_idx:end_idx]
            expert_out = expert(tokens_for_this_expert)
            outputs.append(expert_out)
            start_idx = end_idx

        outs = torch.cat(outputs, dim=0) if len(outputs) else sorted_tokens.new_empty(0)
        new_x = torch.empty_like(outs)
        new_x[idxs] = outs
        final_out = (
            new_x.view(*topk_ids.shape, -1)
            .type(topk_weight.dtype)
            .mul_(topk_weight.unsqueeze(dim=-1))
            .sum(dim=1)
            .type(new_x.dtype)
        )
        return final_out


# Copied from transformers.models.llama.modeling_llama.repeat_kv
def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
    """
    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
    """
    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
    if n_rep == 1:
        return hidden_states
    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)


def init_rotary_embeddings(config, head_dim, max_position_embeddings, rope_theta):
    """Shared function to initialize rotary embeddings"""
    if config.rope_scaling is None:
        return BailingMoeRotaryEmbedding(
            head_dim,
            max_position_embeddings=max_position_embeddings,
            base=rope_theta,
        )
    else:
        scaling_type = config.rope_scaling["type"]
        scaling_factor = config.rope_scaling["factor"]
        if scaling_type == "linear":
            return BailingMoeLinearScalingRotaryEmbedding(
                head_dim,
                max_position_embeddings=max_position_embeddings,
                scaling_factor=scaling_factor,
                base=rope_theta,
            )
        elif scaling_type == "dynamic":
            return BailingMoeDynamicNTKScalingRotaryEmbedding(
                head_dim,
                max_position_embeddings=max_position_embeddings,
                scaling_factor=scaling_factor,
                base=rope_theta,
            )
        elif scaling_type == "yarn":
            kwargs = {
                key: config.rope_scaling[key]
                for key in [
                    "original_max_position_embeddings",
                    "beta_fast",
                    "beta_slow",
                    "mscale",
                    "mscale_all_dim",
                ]
                if key in config.rope_scaling
            }
            return BailingMoeYarnRotaryEmbedding(
                head_dim,
                max_position_embeddings=max_position_embeddings,
                scaling_factor=scaling_factor,
                base=rope_theta,
                **kwargs,
            )
        else:
            raise ValueError(f"Unknown RoPE scaling type {scaling_type}")


def build_slope_tensor(n_attention_heads: int):
    """
    Build a tensor of slopes for Lightning Attention-2 as described in the paper:
    "Lightning Attention-2: A Free Lunch for Handling Unlimited Sequence Lengths in Large Language Models"
    (https://arxiv.org/abs/2401.04658)
    
    This function computes the slope values that control the decay rate of attention scores
    based on the number of attention heads. The slopes are designed to have specific
    mathematical properties that work optimally when the number of heads is a power of 2.
    
    For non-power-of-2 head counts, a workaround is implemented to maintain similar properties.
    
    Args:
        n_attention_heads (int): Number of attention heads in the model
        
    Returns:
        torch.Tensor: A tensor of shape [n_attention_heads] containing the computed slopes
        
    Note:
        Code copied from: https://github.com/OpenNLPLab/lightning-attention/blob/d15c38529bbd5c2c82b44ddda3cac885825aa873/lightning_attn/utils/utils.py#L6
    """    
    def get_slopes(n):
        def get_slopes_power_of_2(n):
            start = 2 ** (-(2 ** -(math.log2(n) - 3)))
            ratio = start
            return [start * ratio ** i for i in range(n)]

        if math.log2(n).is_integer():
            return get_slopes_power_of_2(
                n)  # In the paper, we only train models that have 2^a heads for some a. This function has
        else:  # some good properties that only occur when the input is a power of 2. To maintain that even
            closest_power_of_2 = 2 ** math.floor(
                math.log2(n))  # when the number of heads is not a power of 2, we use this workaround.
            return (get_slopes_power_of_2(closest_power_of_2)
                    + get_slopes(2 * closest_power_of_2)[0::2][:n - closest_power_of_2])

    slopes = torch.tensor(get_slopes(n_attention_heads), dtype=torch.float)
    return slopes


class BailingMoeLinearAttention(nn.Module):
    """
    BailingMoeLinearAttention implements a linear attention mechanism based on Lightning Attention-2
    (https://arxiv.org/abs/2401.04658) with efficient computation using flash-linear-attention operators.
    
    The implementation leverages optimized kernels from the flash-linear-attention library
    (https://github.com/fla-org/flash-linear-attention) for maximum performance.
    """
    def __init__(
        self,
        config: BailingMoeLinearConfig,
        mode: str = 'chunk',
        hidden_size: int = 1024,
        expand_k: float = 1.0,
        expand_v: float = 1.0,
        head_dim: int = 128,
        num_heads: int = 8,
        num_kv_heads: Optional[int] = None,
        feature_map: Optional[str] = None,
        use_output_gate: bool = True,
        gate_fn: str = 'swish',
        norm_eps: float = 1e-5,
        layer_idx: int = None,
        num_layers: int = None,
        use_low_rank: bool = False,
        rotary_type: str = 'none'
    ):
        super().__init__()
        self.mode = mode
        self.hidden_size = hidden_size
        self.expand_k = expand_k
        self.expand_v = expand_v
        self.head_dim = head_dim
        self.num_heads = num_heads
        self.num_kv_heads = num_kv_heads if num_kv_heads is not None else num_heads
        self.num_kv_groups = self.num_heads // self.num_kv_heads
        self.feature_map_fn = ACT2FN[feature_map] if feature_map is not None else None
        self.use_output_gate = use_output_gate

        self.key_dim = int(hidden_size * expand_k)
        self.value_dim = int(hidden_size * expand_v)
        self.layer_idx = layer_idx
        self.num_layers = num_layers
        self.max_position_embeddings = config.max_position_embeddings
        self.rope_theta = config.rope_theta

        assert mode in ['chunk', 'fused_chunk', 'parallel', 'fused_recurrent'], f"Not suppoerted mode `{mode}`."
        assert self.key_dim % num_heads == 0, f"key dim must be divisible by num_heads of {num_heads}"
        assert self.value_dim % num_heads == 0, f"value dim must be divisible by num_heads of {num_heads}"

        if self.head_dim is not None:
            self.head_qk_dim = self.head_dim
            self.head_v_dim = self.head_dim
        else:
            self.head_qk_dim = self.key_dim // num_heads
            self.head_v_dim = self.value_dim // num_heads

        self.query_key_value = nn.Linear(
            hidden_size,
            self.num_heads * self.head_qk_dim + self.num_kv_heads * self.head_qk_dim + self.num_kv_heads * self.head_v_dim,
            bias=False
        )
        if self.use_output_gate:
            if use_low_rank:
                self.g_proj = nn.Sequential(
                    nn.Linear(hidden_size, self.head_qk_dim, bias=False),
                    nn.Linear(self.head_qk_dim, self.num_heads * self.head_v_dim, bias=False),
                )
            else:
                self.g_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_v_dim, bias=False)
        self.rotary_emb = init_rotary_embeddings(config, self.head_qk_dim, self.max_position_embeddings, self.rope_theta)

        self.linear_rope = config.linear_rope
        self.use_linear_silu = config.use_linear_silu
        self.rotary_type = rotary_type
        self.dense = nn.Linear(self.num_heads * self.head_v_dim, hidden_size, bias=False)

        self.g_norm = BailingMoeRMSNorm(hidden_size=self.num_heads * self.head_v_dim, eps=norm_eps)
        self.gate_fn = ACT2FN[gate_fn]
        self.linear_scale = None
        self.lightning_attn_ops = {
            'fused_recurrent': fused_recurrent_simple_gla,
            'chunk': chunk_simple_gla
        }

    def forward(
        self,
        hidden_states: torch.Tensor, # [b, s, h]
        attention_mask: Optional[torch.Tensor] = None, # [b, s]
        past_key_value: Optional[Tuple[torch.Tensor]] = None,
        position_ids=None,
        use_cache: Optional[bool] = False,
        output_attentions: Optional[bool] = False,
        **kwargs
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Cache]]:
        if attention_mask is not None:
            assert len(attention_mask.shape) == 2, (
                "Expected attention_mask as a 0-1 matrix with shape [batch_size, seq_len] "
                "for padding purposes (0 indicating padding). "
                "Arbitrary attention masks of shape [batch_size, seq_len, seq_len] are not allowed."
            )

        # launching the triton kernel for just one token will actually be slower
        mode = 'fused_recurrent' if hidden_states.shape[1] <= 64 else self.mode

        # Currently output_attentions can only be False, returning attention weights is not supported
        assert not output_attentions, "output_attentions can only be False, returning attention weights is not supported"
        
        qkv = self.query_key_value(hidden_states)
        if self.use_linear_silu:
            qkv = F.silu(qkv)
        q, k, v = torch.split(qkv, [
            self.num_heads * self.head_qk_dim,
            self.num_kv_heads * self.head_qk_dim,
            self.num_kv_heads * self.head_v_dim
        ], dim=-1)
        device = hidden_states.device

        recurrent_state = None
        if past_key_value is not None and isinstance(past_key_value, Cache):
            # ensure the cache list is long enough
            while len(past_key_value.key_cache) <= self.layer_idx:
                past_key_value.key_cache.append(None)
                past_key_value.value_cache.append(None)
            
            # check if there is a state for this layer
            if past_key_value.key_cache[self.layer_idx] is not None:
                recurrent_state = past_key_value.key_cache[self.layer_idx]
                # ensure recurrent_state is on the same device as hidden_states
                if recurrent_state.device != hidden_states.device:
                    recurrent_state = recurrent_state.to(device).contiguous()

        if recurrent_state is None:
            # dealing with left-padding
            if attention_mask is not None and use_cache:
                v = v.mul_(attention_mask[:, -v.shape[-2]:, None])

        q = rearrange(q, '... (h d) -> ... h d', h=self.num_heads)
        k = rearrange(k, '... (h d) -> ... h d', h=self.num_kv_heads)

        rotary_cos, rotary_sin = self.rotary_emb(hidden_states, seq_len=position_ids.max() + 1)
        rotary_emb = (rotary_cos, rotary_sin)

        if self.linear_rope:
            if self.rotary_type in ['full-1d']:
                (cos, sin) = rotary_emb
                # Support fot multi GPU inference
                if cos.device != hidden_states.device:
                    cos = cos.to(hidden_states.device)
                    sin = sin.to(hidden_states.device)

                q, k = apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=2)
                q = q.to(v.dtype)
                k = k.to(v.dtype)
            else:
                raise ValueError(f"Unsupported rotary type: {self.rotary_type}")

        if self.num_kv_groups > 1:
            k = repeat(k, 'b t h d -> b t (h g) d', h=self.num_kv_heads, g=self.num_kv_groups)
            v = repeat(v, 'b t (h d) -> b t (h g) d', h=self.num_kv_heads, g=self.num_kv_groups)
        else:
            v = rearrange(v, 'b t (h d) -> b t h d', h=self.num_kv_heads)

        H = q.shape[2]
        s = -build_slope_tensor(H) * (1 - self.layer_idx / (self.num_layers - 1) + 1e-5)
        g = s[None, None, :].expand(q.shape[0], q.shape[1], q.shape[2]).contiguous()
        
        q = q.to(device)
        k = k.to(device)
        v = v.to(device)
        g = g.to(device)

        if mode in self.lightning_attn_ops:
            o, recurrent_state = self.lightning_attn_ops[mode](
                q=q,
                k=k,
                v=v,
                g=g,
                scale=self.linear_scale,
                initial_state=recurrent_state,
                output_final_state=use_cache,
                head_first=False
            )
        else:
            raise NotImplementedError(f"Not supported mode `{mode}`.")
        o = o.to(hidden_states.dtype)
        o = rearrange(o, 'b t h d -> b t (h d)')
        o = self.g_norm(o)
        g = self.g_proj(hidden_states)
        o = o * F.sigmoid(g)
        o = self.dense(o)

        # update DynamicCache
        if use_cache and past_key_value is not None and isinstance(past_key_value, Cache):
            target_device = None
            for cache in past_key_value.key_cache:
                if cache is not None:
                    target_device = cache.device
                    break
            if target_device is None:
                target_device = recurrent_state.device
            
            # move to target device
            if recurrent_state.device != target_device:
                recurrent_state = recurrent_state.to(target_device)
    
            past_key_value.key_cache[self.layer_idx] = recurrent_state
            past_key_value.value_cache[self.layer_idx] = None

            if self.layer_idx == 0:
                # update seen_tokens
                past_key_value._seen_tokens += hidden_states.shape[1]

        if not output_attentions:
            attn_weights = None
        return o, attn_weights, past_key_value


# Copied from transformers.models.llama.modeling_llama.LlamaAttention with Llama->BailingMoe
class BailingMoeAttention(nn.Module):
    """Multi-headed attention from 'Attention Is All You Need' paper"""

    def __init__(self, config: BailingMoeLinearConfig, layer_idx: Optional[int] = None):
        super().__init__()
        self.config = config
        self.layer_idx = layer_idx
        if layer_idx is None:
            logger.warning_once(
                f"Instantiating {self.__class__.__name__} without passing `layer_idx` is not recommended and will "
                "to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` "
                "when creating this class."
            )

        self.attention_dropout = config.attention_dropout
        self.hidden_size = config.hidden_size
        self.num_heads = config.num_attention_heads
        self.head_dim = config.head_dim or self.hidden_size // self.num_heads
        self.num_key_value_heads = config.num_key_value_heads
        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
        self.max_position_embeddings = config.max_position_embeddings
        self.rope_theta = config.rope_theta
        self.is_causal = True

        self.query_key_value = nn.Linear(
            self.hidden_size,
            (self.num_heads + 2 * self.num_key_value_heads) * self.head_dim,
            bias=config.use_qkv_bias,
        )
        self.dense = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=config.use_bias)
        self.rotary_emb = init_rotary_embeddings(config, self.head_dim, self.max_position_embeddings, self.rope_theta)

    def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
        return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_value: Optional[Cache] = None,
        output_attentions: bool = False,
        use_cache: bool = False,
        **kwargs,
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
        if "padding_mask" in kwargs:
            warnings.warn(
                "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
            )

        bsz, q_len, _ = hidden_states.size()

        qkv = self.query_key_value(hidden_states)
        qkv = qkv.view(bsz, q_len, self.num_heads + 2 * self.num_key_value_heads, self.head_dim)

        query_states, key_states, value_states = qkv.split(
            [self.num_heads, self.num_key_value_heads, self.num_key_value_heads], dim=-2
        )
        query_states = query_states.transpose(1, 2)
        key_states = key_states.transpose(1, 2)
        value_states = value_states.transpose(1, 2)

        kv_seq_len = key_states.shape[-2]
        if past_key_value is not None:
            if self.layer_idx is None:
                raise ValueError(
                    f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
                    "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
                    "with a layer index."
                )
            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)

        if past_key_value is not None:
            cache_kwargs = {"sin": sin, "cos": cos}  # Specific to RoPE models
            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)

        key_states = repeat_kv(key_states, self.num_key_value_groups)
        value_states = repeat_kv(value_states, self.num_key_value_groups)

        attn_weights = torch.matmul(query_states / math.sqrt(self.head_dim), key_states.transpose(2, 3))

        if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
            raise ValueError(
                f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
                f" {attn_weights.size()}"
            )

        if attention_mask is not None:
            if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
                raise ValueError(
                    f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
                )
            attn_weights = attn_weights + attention_mask

        # upcast attention to fp32
        attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
        attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
        attn_output = torch.matmul(attn_weights, value_states)

        if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
            raise ValueError(
                f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
                f" {attn_output.size()}"
            )

        attn_output = attn_output.transpose(1, 2).contiguous()

        attn_output = attn_output.reshape(bsz, q_len, -1)

        attn_output = self.dense(attn_output)

        if not output_attentions:
            attn_weights = None

        return attn_output, attn_weights, past_key_value


# Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2 with Llama->BailingMoe
class BailingMoeFlashAttention2(BailingMoeAttention):
    """
    BailingMoe flash attention module. This module inherits from `BailingMoeAttention` as the weights of the module stays
    untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
    flash attention and deal with padding tokens in case the input contains any of them.
    """

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)

        # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
        # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
        # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
        self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.LongTensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_value: Optional[Cache] = None,
        output_attentions: bool = False,
        use_cache: bool = False,
        **kwargs,
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
        # BailingMoeFlashAttention2 attention does not support output_attentions
        if "padding_mask" in kwargs:
            warnings.warn(
                "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
            )

            # overwrite attention_mask with padding_mask
            attention_mask = kwargs.pop("padding_mask")

        output_attentions = False

        bsz, q_len, _ = hidden_states.size()

        # Flash attention requires the input to have the shape
        # batch_size x seq_length x head_dim x hidden_dim
        # therefore we just need to keep the original shape

        qkv = self.query_key_value(hidden_states)
        qkv = qkv.view(bsz, q_len, self.num_heads + 2 * self.num_key_value_heads, self.head_dim)

        query_states, key_states, value_states = qkv.split(
            [self.num_heads, self.num_key_value_heads, self.num_key_value_heads], dim=-2
        )
        query_states = query_states.transpose(1, 2)
        key_states = key_states.transpose(1, 2)
        value_states = value_states.transpose(1, 2)

        kv_seq_len = key_states.shape[-2]
        if past_key_value is not None:
            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)

        if past_key_value is not None:
            cache_kwargs = {"sin": sin, "cos": cos}  # Specific to RoPE models
            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)

        # TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache
        # to be able to avoid many of these transpose/reshape/view.
        query_states = query_states.transpose(1, 2)
        key_states = key_states.transpose(1, 2)
        value_states = value_states.transpose(1, 2)

        dropout_rate = self.attention_dropout if self.training else 0.0

        # In PEFT, usually we cast the layer norms in float32 for training stability reasons
        # therefore the input hidden states gets silently cast in float32. Hence, we need
        # cast them back in the correct dtype just to be sure everything works as expected.
        # This might slow down training & inference so it is recommended to not cast the LayerNorms
        # in fp32. (BailingMoeRMSNorm handles it correctly)

        input_dtype = query_states.dtype
        if input_dtype == torch.float32:
            # Handle the case where the model is quantized
            if hasattr(self.config, "_pre_quantization_dtype"):
                target_dtype = self.config._pre_quantization_dtype
            elif torch.is_autocast_enabled():
                target_dtype = torch.get_autocast_gpu_dtype()
            else:
                target_dtype = self.q_proj.weight.dtype

            logger.warning_once(
                f"The input hidden states seems to be silently casted in float32, this might be related to"
                f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
                f" {target_dtype}."
            )

            query_states = query_states.to(target_dtype)
            key_states = key_states.to(target_dtype)
            value_states = value_states.to(target_dtype)

        attn_output = self._flash_attention_forward(
            query_states, key_states, value_states, attention_mask, q_len, dropout=dropout_rate
        )

        attn_output = attn_output.reshape(bsz, q_len, -1).contiguous()
        attn_output = self.dense(attn_output)

        if not output_attentions:
            attn_weights = None

        return attn_output, attn_weights, past_key_value

    def _flash_attention_forward(
        self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None
    ):
        """
        Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
        first unpad the input, then computes the attention scores and pad the final attention scores.

        Args:
            query_states (`torch.Tensor`):
                Input query states to be passed to Flash Attention API
            key_states (`torch.Tensor`):
                Input key states to be passed to Flash Attention API
            value_states (`torch.Tensor`):
                Input value states to be passed to Flash Attention API
            attention_mask (`torch.Tensor`):
                The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
                position of padding tokens and 1 for the position of non-padding tokens.
            dropout (`int`, *optional*):
                Attention dropout
            softmax_scale (`float`, *optional*):
                The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
            query_length (`int`):
                The length of the query sequence in terms of tokens. This represents the number of tokens in the
                `query_states` tensor along the sequence dimension. It is used to determine the effective sequence
                length for attention computations.
        """
        if not self._flash_attn_uses_top_left_mask:
            causal = self.is_causal
        else:
            # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in BailingMoeFlashAttention2 __init__.
            causal = self.is_causal and query_length != 1

        # Contains at least one padding token in the sequence
        if attention_mask is not None:
            batch_size = query_states.shape[0]
            query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(
                query_states, key_states, value_states, attention_mask, query_length
            )

            cu_seqlens_q, cu_seqlens_k = cu_seq_lens
            max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens

            attn_output_unpad = flash_attn_varlen_func(
                query_states,
                key_states,
                value_states,
                cu_seqlens_q=cu_seqlens_q,
                cu_seqlens_k=cu_seqlens_k,
                max_seqlen_q=max_seqlen_in_batch_q,
                max_seqlen_k=max_seqlen_in_batch_k,
                dropout_p=dropout,
                softmax_scale=softmax_scale,
                causal=causal,
            )

            attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
        else:
            attn_output = flash_attn_func(
                query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal
            )

        return attn_output

    def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
        indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
        batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape

        key_layer = index_first_axis(
            key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
        )
        value_layer = index_first_axis(
            value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
        )
        if query_length == kv_seq_len:
            query_layer = index_first_axis(
                query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k
            )
            cu_seqlens_q = cu_seqlens_k
            max_seqlen_in_batch_q = max_seqlen_in_batch_k
            indices_q = indices_k
        elif query_length == 1:
            max_seqlen_in_batch_q = 1
            cu_seqlens_q = torch.arange(
                batch_size + 1, dtype=torch.int32, device=query_layer.device
            )  # There is a memcpy here, that is very bad.
            indices_q = cu_seqlens_q[:-1]
            query_layer = query_layer.squeeze(1)
        else:
            # The -q_len: slice assumes left padding.
            attention_mask = attention_mask[:, -query_length:]
            query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)

        return (
            query_layer,
            key_layer,
            value_layer,
            indices_q,
            (cu_seqlens_q, cu_seqlens_k),
            (max_seqlen_in_batch_q, max_seqlen_in_batch_k),
        )


# Copied from transformers.models.llama.modeling_llama.LlamaSdpaAttention with Llama->BailingMoe
class BailingMoeSdpaAttention(BailingMoeAttention):
    """
    BailingMoe attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
    `BailingMoeAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
    SDPA API.
    """

    # Adapted from BailingMoeAttention.forward
    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_value: Optional[Cache] = None,
        output_attentions: bool = False,
        use_cache: bool = False,
        **kwargs,
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
        if output_attentions:
            # TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
            logger.warning_once(
                "BailingMoeLinearModel is using BailingMoeSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, "
                'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
            )
            return super().forward(
                hidden_states=hidden_states,
                attention_mask=attention_mask,
                position_ids=position_ids,
                past_key_value=past_key_value,
                output_attentions=output_attentions,
                use_cache=use_cache,
            )

        bsz, q_len, _ = hidden_states.size()

        qkv = self.query_key_value(hidden_states)
        qkv = qkv.view(bsz, q_len, self.num_heads + 2 * self.num_key_value_heads, self.head_dim)

        query_states, key_states, value_states = qkv.split(
            [self.num_heads, self.num_key_value_heads, self.num_key_value_heads], dim=-2
        )
        query_states = query_states.transpose(1, 2)
        key_states = key_states.transpose(1, 2)
        value_states = value_states.transpose(1, 2)

        kv_seq_len = key_states.shape[-2]
        if past_key_value is not None:
            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)

        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)

        if past_key_value is not None:
            cache_kwargs = {"sin": sin, "cos": cos}  # Specific to RoPE models
            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)

        key_states = repeat_kv(key_states, self.num_key_value_groups)
        value_states = repeat_kv(value_states, self.num_key_value_groups)

        if attention_mask is not None:
            if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
                raise ValueError(
                    f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
                )

        # SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
        # Reference: https://github.com/pytorch/pytorch/issues/112577.
        if query_states.device.type == "cuda" and attention_mask is not None:
            query_states = query_states.contiguous()
            key_states = key_states.contiguous()
            value_states = value_states.contiguous()

        attn_output = torch.nn.functional.scaled_dot_product_attention(
            query_states,
            key_states,
            value_states,
            attn_mask=attention_mask,
            dropout_p=self.attention_dropout if self.training else 0.0,
            # The q_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case q_len == 1.
            is_causal=self.is_causal and attention_mask is None and q_len > 1,
        )

        attn_output = attn_output.transpose(1, 2).contiguous()
        attn_output = attn_output.reshape(bsz, q_len, -1)

        attn_output = self.dense(attn_output)

        return attn_output, None, past_key_value


BAILING_MOE_ATTENTION_CLASSES = {
    "eager": BailingMoeAttention,
    "flash_attention_2": BailingMoeFlashAttention2,
    "sdpa": BailingMoeSdpaAttention,
}


class BailingMoeLinearDecoderLayer(nn.Module):
    def __init__(self, config: BailingMoeLinearConfig, layer_idx: int):
        super().__init__()
        self.hidden_size = config.hidden_size
        self.layer_group_size = config.layer_group_size

        # Use standard Attention if layer_idx+1 is divisible by layer_group_size or if layer_idx exceeds
        # the threshold (num_hidden_layers // layer_group_size * layer_group_size), otherwise use linear attention
        self.attention_layer_type = "attention" if (layer_idx + 1) % config.layer_group_size == 0 or \
            layer_idx >= config.num_hidden_layers // config.layer_group_size * config.layer_group_size else "linear_attention"
        
        if self.attention_layer_type == "attention":
            self.attention = BAILING_MOE_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx)
        else:
            self.head_dim = config.head_dim or config.hidden_size // config.num_attention_heads
            self.use_linear_gqa = config.use_linear_gqa
            self.linear_mode = config.linear_mode
            self.attention = BailingMoeLinearAttention(
                config=config,
                mode=self.linear_mode,
                hidden_size=self.hidden_size,
                expand_k=1,
                expand_v=1,
                head_dim=self.head_dim,
                num_heads=config.num_attention_heads,
                num_kv_heads=config.num_key_value_heads if self.use_linear_gqa else None,
                feature_map=None,
                use_output_gate=True,
                gate_fn="swish",
                norm_eps=config.rms_norm_eps,
                layer_idx=layer_idx,
                num_layers=config.num_hidden_layers,
                use_low_rank=config.use_low_rank,
                rotary_type=config.rotary_type,
            )        
        self.mlp = (
            BailingMoeSparseMoeBlock(config)
            if (config.num_experts is not None and layer_idx >= config.first_k_dense_replace)
            else BailingMoeMLP(config=config, intermediate_size=config.intermediate_size)
        )
        self.layer_idx = layer_idx
        self.input_layernorm = BailingMoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.post_attention_layernorm = BailingMoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps)

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_value: Optional[Tuple[torch.Tensor]] = None,
        output_attentions: Optional[bool] = False,
        output_router_logits: Optional[bool] = False,
        use_cache: Optional[bool] = False,
        **kwargs,
    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
        """
        Args:
            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
            attention_mask (`torch.FloatTensor`, *optional*):
                attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
                query_sequence_length, key_sequence_length)` if default attention is used.
            position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
                Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
                config.n_positions - 1]`.
            past_key_value (`Tuple(torch.FloatTensor)`, *optional*):
                cached past key and value projection states
            output_attentions (`bool`, *optional*):
                Whether to return the attentions tensors of all attention layers. See `attentions` under
                returned tensors for more detail.
            output_router_logits (`bool`, *optional*):
                Whether or not to return the logits of all the routers. They are useful for computing the router loss,
                and should not be returned during inference.
            use_cache (`bool`, *optional*):
                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
                (see `past_key_values`).
        """
        if "padding_mask" in kwargs:
            warnings.warn(
                "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
            )
        residual = hidden_states
        hidden_states = self.input_layernorm(hidden_states)

        if self.attention_layer_type == "attention":
            # Self Attention
            hidden_states, self_attn_weights, present_key_value = self.attention(
                hidden_states=hidden_states,
                attention_mask=attention_mask,
                position_ids=position_ids,
                past_key_value=past_key_value,
                output_attentions=output_attentions,
                use_cache=use_cache,
            )
        else:
            # Linear Attention
            batch_size, seq_len = hidden_states.shape[0], hidden_states.shape[1]
            device = hidden_states.device
            if attention_mask is None:
                # if attention_mask is None, create a full mask
                attention_mask = torch.ones((batch_size, seq_len), dtype=torch.int32, device=device)
            elif attention_mask.dim() == 0:
                mask_value = attention_mask.item()
                attention_mask = torch.full((batch_size, seq_len), mask_value, dtype=torch.int32, device=device)
            elif attention_mask.dim() == 4 and attention_mask.shape[1] == 1:
                attention_mask = attention_mask[:, 0, -1, :].to(torch.int32)
                # the attention mask is additive mask, which means the masked position is a large negative number, and the unmasked position is 0
                attention_mask = (attention_mask > -1e4).to(torch.int32)
            else:
                raise ValueError(f"Unsupported mask dimension: {attention_mask.shape}")

            hidden_states, self_attn_weights, present_key_value = self.attention(
                hidden_states=hidden_states,
                attention_mask=attention_mask,
                past_key_value=past_key_value,
                position_ids=position_ids,
                use_cache=use_cache,
                output_attentions=output_attentions,
            )

        hidden_states = residual + hidden_states

        # Fully Connected
        residual = hidden_states
        hidden_states = self.post_attention_layernorm(hidden_states)
        hidden_states = self.mlp(hidden_states)

        if isinstance(hidden_states, tuple):
            hidden_states, router_logits = hidden_states
        else:
            router_logits = None
        hidden_states = residual + hidden_states

        outputs = (hidden_states,)

        if output_attentions:
            outputs += (self_attn_weights,)

        if use_cache:
            outputs += (present_key_value,)

        if output_router_logits:
            outputs += (router_logits,)

        return outputs


BAILINGMOELINEAR_START_DOCSTRING = r"""
    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
    etc.)

    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
    and behavior.

    Parameters:
        config ([`BailingMoeLinearConfig`]):
            Model configuration class with all the parameters of the model. Initializing with a config file does not
            load the weights associated with the model, only the configuration. Check out the
            [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""


@add_start_docstrings(
    "The bare BailingMoeLinear Model outputting raw hidden-states without any specific head on top.",
    BAILINGMOELINEAR_START_DOCSTRING,
)
class BailingMoeLinearPreTrainedModel(PreTrainedModel):
    config_class = BailingMoeLinearConfig
    base_model_prefix = "model"
    supports_gradient_checkpointing = True
    _no_split_modules = ["BailingMoeLinearDecoderLayer"]
    _skip_keys_device_placement = "past_key_values"
    _supports_flash_attn_2 = True
    _supports_sdpa = True
    _supports_cache_class = True

    def _init_weights(self, module):
        std = self.config.initializer_range
        if isinstance(module, nn.Linear):
            module.weight.data.normal_(mean=0.0, std=std)
            if module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, nn.Embedding):
            module.weight.data.normal_(mean=0.0, std=std)
            if module.padding_idx is not None:
                module.weight.data[module.padding_idx].zero_()


BAILINGMOE_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
            it.

            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

            - 1 for tokens that are **not masked**,
            - 0 for tokens that are **masked**.

            [What are attention masks?](../glossary#attention-mask)

            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            If `past_key_values` is used, optionally only the last `input_ids` have to be input (see
            `past_key_values`).

            If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
            and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
            information on the default strategy.

            - 1 indicates the head is **not masked**,
            - 0 indicates the head is **masked**.
        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
            config.n_positions - 1]`.

            [What are position IDs?](../glossary#position-ids)
        past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
            Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
            blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
            returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.

            Two formats are allowed:
            - a [`~cache_utils.Cache`] instance;
            - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
            shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy
            cache format.

            The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
            legacy cache format will be returned.

            If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
            have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
            of shape `(batch_size, sequence_length)`.
        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
            model's internal embedding lookup matrix.
        use_cache (`bool`, *optional*):
            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
            `past_key_values`).
        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
            tensors for more detail.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""


@add_start_docstrings(
    "The bare BailingMoeLinear Model outputting raw hidden-states without any specific head on top.",
    BAILINGMOELINEAR_START_DOCSTRING,
)
class BailingMoeLinearModel(BailingMoeLinearPreTrainedModel):
    """
    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`BailingMoeLinearDecoderLayer`]

    Args:
        config: BailingMoeLinearConfig
    """

    def __init__(self, config: BailingMoeLinearConfig):
        super().__init__(config)
        self.padding_idx = config.pad_token_id
        self.vocab_size = config.vocab_size

        self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
        self.layers = nn.ModuleList(
            [BailingMoeLinearDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
        )
        
        # find a standard Attention layer_idx for later sequence length calculation
        self.standard_attn_layer_idx = 0
        for layer_idx, layer in enumerate(self.layers):
            if hasattr(layer, 'attention_layer_type') and layer.attention_layer_type == "attention":
                self.standard_attn_layer_idx = layer_idx
                break

        self._use_sdpa = config._attn_implementation == "sdpa"
        self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
        self.norm = BailingMoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps)

        self.gradient_checkpointing = False
        # Initialize weights and apply final processing
        self.post_init()

    def get_input_embeddings(self):
        return self.word_embeddings

    def set_input_embeddings(self, value):
        self.word_embeddings = value

    @add_start_docstrings_to_model_forward(BAILINGMOE_INPUTS_DOCSTRING)
    def forward(
        self,
        input_ids: torch.LongTensor = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[List[torch.FloatTensor]] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        output_router_logits: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        **kwargs,
    ) -> Union[Tuple, MoeModelOutputWithPast]:
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        output_router_logits = (
            output_router_logits if output_router_logits is not None else self.config.output_router_logits
        )
        use_cache = use_cache if use_cache is not None else self.config.use_cache

        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        # retrieve input_ids and inputs_embeds
        if input_ids is not None and inputs_embeds is not None:
            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
        elif input_ids is not None:
            batch_size, seq_length = input_ids.shape[:2]
        elif inputs_embeds is not None:
            batch_size, seq_length = inputs_embeds.shape[:2]
        else:
            raise ValueError("You have to specify either input_ids or inputs_embeds")

        if self.gradient_checkpointing and self.training:
            if use_cache:
                logger.warning_once(
                    "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`transformers."
                )
                use_cache = False

        past_key_values_length = 0

        if use_cache:
            use_legacy_cache = not isinstance(past_key_values, Cache)
            if use_legacy_cache:
                past_key_values = DynamicCache.from_legacy_cache(past_key_values)
            past_key_values_length = past_key_values.get_usable_length(seq_length, layer_idx=self.standard_attn_layer_idx)

        if position_ids is None:
            device = input_ids.device if input_ids is not None else inputs_embeds.device
            position_ids = torch.arange(
                past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
            )
            position_ids = position_ids.unsqueeze(0)

        if inputs_embeds is None:
            inputs_embeds = self.word_embeddings(input_ids)

        if self._use_flash_attention_2:
            # 2d mask is passed through the layers
            attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
        elif self._use_sdpa and not output_attentions:
            # output_attentions=True can not be supported when using SDPA, and we fall back on
            # the manual implementation that requires a 4D causal mask in all cases.
            attention_mask = _prepare_4d_causal_attention_mask_for_sdpa(
                attention_mask,
                (batch_size, seq_length),
                inputs_embeds,
                past_key_values_length,
            )
        else:
            # 4d mask is passed through the layers
            attention_mask = _prepare_4d_causal_attention_mask(
                attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
            )

        # embed positions
        hidden_states = inputs_embeds

        # decoder layers
        all_hidden_states = () if output_hidden_states else None
        all_self_attns = () if output_attentions else None
        all_router_logits = () if output_router_logits else None
        next_decoder_cache = None

        for decoder_layer in self.layers:
            if output_hidden_states:
                all_hidden_states += (hidden_states,)

            if self.gradient_checkpointing and self.training:
                layer_outputs = self._gradient_checkpointing_func(
                    decoder_layer.__call__,
                    hidden_states,
                    attention_mask,
                    position_ids,
                    past_key_values,
                    output_attentions,
                    output_router_logits,
                    use_cache,
                )
            else:
                layer_outputs = decoder_layer(
                    hidden_states,
                    attention_mask=attention_mask,
                    position_ids=position_ids,
                    past_key_value=past_key_values,
                    output_attentions=output_attentions,
                    output_router_logits=output_router_logits,
                    use_cache=use_cache,
                )
            hidden_states = layer_outputs[0]

            if use_cache:
                next_decoder_cache = layer_outputs[2 if output_attentions else 1]

            if output_attentions:
                all_self_attns += (layer_outputs[1],)

            if output_router_logits and layer_outputs[-1] is not None:
                all_router_logits += (layer_outputs[-1],)

        hidden_states = self.norm(hidden_states)

        # add hidden states from the last decoder layer
        if output_hidden_states:
            all_hidden_states += (hidden_states,)

        next_cache = None
        if use_cache:
            next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache
        if not return_dict:
            return tuple(
                v
                for v in [hidden_states, next_cache, all_hidden_states, all_self_attns, all_router_logits]
                if v is not None
            )
        return MoeModelOutputWithPast(
            last_hidden_state=hidden_states,
            past_key_values=next_cache,
            hidden_states=all_hidden_states,
            attentions=all_self_attns,
            router_logits=all_router_logits,
        )


class BailingMoeLinearForCausalLM(BailingMoeLinearPreTrainedModel):
    _tied_weights_keys = ["lm_head.weight"]

    def __init__(self, config: BailingMoeLinearConfig):
        super().__init__(config)
        self.model = BailingMoeLinearModel(config)
        self.vocab_size = config.vocab_size
        self.norm_head = config.norm_head
        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
        self.standard_attn_layer_idx = self.model.standard_attn_layer_idx

        # Initialize weights and apply final processing
        self.post_init()

    def get_input_embeddings(self):
        return self.model.word_embeddings

    def set_input_embeddings(self, value):
        self.model.word_embeddings = value

    def get_output_embeddings(self):
        return self.lm_head

    def set_output_embeddings(self, new_embeddings):
        self.lm_head = new_embeddings

    def set_decoder(self, decoder):
        self.model = decoder

    def get_decoder(self):
        return self.model

    def compute_logit(self, hidden_states):
        if self.norm_head:
            if self.training:
                norm_weight = (
                    self.lm_head.weight / (torch.norm(self.lm_head.weight, p=2, dim=0, keepdim=True) + 1e-7).detach()
                )
                logits = F.linear(hidden_states, norm_weight, None)
            else:
                self.lm_head.weight.data = (
                    self.lm_head.weight.data.float()
                    / (torch.norm(self.lm_head.weight.data.float(), p=2, dim=0, keepdim=True) + 1e-7)
                ).to(hidden_states.dtype)
                logits = F.linear(hidden_states, self.lm_head.weight.data, None)
                self.norm_head = False
        else:
            logits = self.lm_head(hidden_states)
        return logits

    @add_start_docstrings_to_model_forward(BAILINGMOE_INPUTS_DOCSTRING)
    @replace_return_docstrings(output_type=MoeCausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
    def forward(
        self,
        input_ids: torch.LongTensor = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[List[torch.FloatTensor]] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        labels: Optional[torch.LongTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        output_router_logits: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        **kwargs,
    ) -> Union[Tuple, MoeCausalLMOutputWithPast]:
        r"""
        Args:
            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
                config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
                (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.

        Returns:

        Example:

        ```python
        >>> from transformers import AutoTokenizer

        >>> model = BailingMoeForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
        >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)

        >>> prompt = "Hey, are you conscious? Can you talk to me?"
        >>> inputs = tokenizer(prompt, return_tensors="pt")

        >>> # Generate
        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
        "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
        ```"""
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        output_router_logits = (
            output_router_logits if output_router_logits is not None else self.config.output_router_logits
        )
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
        outputs = self.model(
            input_ids=input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            output_router_logits=output_router_logits,
            return_dict=return_dict,
            **kwargs,
        )

        hidden_states = outputs[0]

        logits = self.compute_logit(hidden_states=hidden_states)
        logits = logits.float()

        loss = None
        aux_loss = None

        if labels is not None:
            # Shift so that tokens < n predict n
            shift_logits = logits[..., :-1, :].contiguous()
            shift_labels = labels[..., 1:].contiguous()
            # Flatten the tokens
            loss_fct = CrossEntropyLoss()
            shift_logits = shift_logits.view(-1, self.config.vocab_size)
            shift_labels = shift_labels.view(-1)
            # Enable model parallelism
            shift_labels = shift_labels.to(shift_logits.device)
            loss = loss_fct(shift_logits, shift_labels)

        if not return_dict:
            output = (logits,) + outputs[1:]
            if output_router_logits:
                output = (aux_loss,) + output
            return (loss,) + output if loss is not None else output

        return MoeCausalLMOutputWithPast(
            loss=loss,
            aux_loss=aux_loss,
            logits=logits,
            past_key_values=outputs.past_key_values,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
            router_logits=outputs.router_logits,
        )

    def prepare_inputs_for_generation(
        self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, token_type_ids=None, **kwargs
    ):
        if past_key_values is not None:
            if isinstance(past_key_values, Cache):
                cache_length = past_key_values.get_seq_length(self.standard_attn_layer_idx)
                past_length = past_key_values.seen_tokens
                max_cache_length = (
                    past_key_values.get_max_length()
                    if hasattr(past_key_values, "get_max_length")
                    else past_key_values.get_max_cache_shape()
                )
            else:
                cache_length = past_length = past_key_values[self.standard_attn_layer_idx][0].shape[2]
                max_cache_length = None

            # Keep only the unprocessed tokens:
            # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
            # some of the inputs are exclusivelly passed as part of the cache (e.g. when passing input_embeds as input)
            if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
                input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
            # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
            # input_ids based on the past_length.
            elif past_length < input_ids.shape[1]:
                input_ids = input_ids[:, past_length:]
            # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.

            # If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
            if (
                max_cache_length is not None
                and attention_mask is not None
                and cache_length + input_ids.shape[1] > max_cache_length
            ):
                attention_mask = attention_mask[:, -max_cache_length:]

        position_ids = kwargs.get("position_ids", None)
        if attention_mask is not None and position_ids is None:
            # create position_ids on the fly for batch generation
            position_ids = attention_mask.long().cumsum(-1) - 1
            position_ids.masked_fill_(attention_mask == 0, 1)
            if past_key_values:
                position_ids = position_ids[:, -input_ids.shape[1] :]

        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
        if inputs_embeds is not None and past_key_values is None:
            model_inputs = {"inputs_embeds": inputs_embeds}
        else:
            model_inputs = {"input_ids": input_ids}

        model_inputs.update(
            {
                "position_ids": position_ids,
                "past_key_values": past_key_values,
                "use_cache": kwargs.get("use_cache"),
                "attention_mask": attention_mask,
            }
        )
        return model_inputs

    @staticmethod
    def _reorder_cache(past_key_values, beam_idx):
        reordered_past = ()
        for layer_past in past_key_values:
            reordered_past += (
                tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
            )
        return reordered_past
